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Feature extraction for hyperspectral image classification

conference contribution
posted on 2023-04-28, 06:28 authored by MD PALASH UDDINMD PALASH UDDIN, MA Mamun, MA Hossain
Remote sensing hyperspectral image (HSI) contains important information of ground surface as a set of hundreds of narrow and contiguous spectral bands. For effective classification of hyperspectral images, feature reduction techniques through feature extraction and feature selection approaches are applied to improve the classification performance. Principal Component Analysis (PCA) is the widely used feature extraction method for dimensionality reduction. In this paper, PCA and its linear variants such as segmented-PCA (SPCA) and folded-PCA (FPCA) together with nonlinear variants kernel-PCA (KPCA) and Kernel Entropy Component Analysis (KECA) have been studied to effectively extract the features for classification task. The feature selection over the new transformed features was carried out using cumulative-variance accumulation based approach except for KECA that employs Renyi entropy based feature selection. The studied methods are compared using real hyperspectral image. The experimental result shows that the classification accuracy of KPCA (95.9245%) and KECA (95.6262%) outperforms FPCA (95.1292%). However, the FPCA provides the less space complexity.

History

Volume

2018-January

Pagination

379-382

Location

Dhaka, Bangladesh

Start date

2017-12-21

End date

2017-12-23

ISBN-13

9781538621752

Language

eng

Title of proceedings

5th IEEE Region 10 Humanitarian Technology Conference 2017, R10-HTC 2017

Event

2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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